Automatic Design of Radial Basis Function Networks Through Enhanced Differential Evolution

During the creation of a classification model, it is vital to keep track of numerous parameters and to produce a model based on the limited knowledge inferred often from very confined data. Methods which aid the construction or completely build the classification model automatically, present a fairly common research interest. This paper proposes an approach that employs differential evolution enhanced through the incorporation of additional knowledge concerning the problem in order to design a radial basis neural network. The knowledge is inferred from the unsupervised learning procedure which aims to ensure an initial population of good solutions. Also, the search space is dynamically adjusted i.e. narrowed during runtime in terms of the decision variables count. The results obtained on several datasets suggest that the proposed approach is able to find well performing networks while keeping the structure simple. Furthermore, a comparison with a differential evolution algorithm without the proposed enhancements and a particle swarm optimization algorithm was carried out illustrating the benefits of the proposed approach.